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Vinay Kumar D.,National Institute of Technology Warangal | Ravi Kumar P.,National Institute of Technology Warangal | Kumari M.S.,Christu Jyoti Institute of Technology and Science
Procedia Engineering | Year: 2013

Different techniques are being attempted over the years to use low pollution emitting fuels in diesel engines to reduce tail pipe emissions with improved engine efficiency. Especially, Biodiesel fuel, derived from different vegetable oils, animal fat and waste cooking oil has received a great attention in the recent past. Transesterification is a proven simplest process to prepare biodiesel in labs with little infrastructure. Application of thermal barrier coatings (TBC) on the engine components is a seriously perused area of interest with low grade fuels like biodiesel fuels. Artificial neural networks (ANN) are gaining popularity to predict the performance and emissions of diesel engines with fairly accurate results besides the thermodynamic models with considerably less complexity and lower computing time. In the present study, experiments have been conducted on a single cylinder diesel engine whose combustion elements are coated with an experimental thermal barrier coating material made from Lanthanum Zirconate. Biodiesel has been prepared from Pongamia Pinnata oil through transesterification process. A series of experiments are conducted on the engine with and without thermal barrier coating using diesel and biodiesel fuels. Performance and emissions data from the experiments is used to train the network with the load, fuel type and coating being the input layer and the brake specific fuel consumption, brake thermal efficiency, CO, HC and NOx emissions being the output layer. Results showed that the coating of engine components with lanthanum zirconate TBC resulted in improved engine efficiency with reduced emissions. ANN model is tested for its accuracy to predict the performance and emissions of the engine with the R values of 0.99 for both the training and test data with a mean square error of 0.002 and a mean relative error of 6.8% © 2013 The Authors. Published by Elsevier Ltd. Source


Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University
Advances in Intelligent and Soft Computing | Year: 2012

Distributed generator (DG) is now commonly used in distribution system to reduce total power loss and to improve the power quality of the network. The major task of connecting DG units is to identify their optimal placement in the system and to evaluate the amount of power to be generated in the DG. By considering this objective, a hybrid technique using Genetic algorithm and Neural-network is proposed in this paper. By placing DGs at optimal locations and by evaluating generating power based on the load requirement, the total power loss in the system can be minimized without affecting the voltage stability of the buses. Due to reduction of total power loss in the system and improvement of bus voltages, the power quality of the system increases. The results show the improved performance of proposed method for different number of DGs connected in the system. © 2012 Springer India Pvt. Ltd. Source


Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Jaya Laxmi A.,JNTUH College of Engineering
International Review on Modelling and Simulations | Year: 2012

DGs are now commonly used in distribution systems to reduce the power disruption in the power system network. Due to the installation of DGs in the system, the total power loss in the can be reduced and voltage profile of the buses and reliability of the system can be improved. The significant process to decrease the total power loss and to improve the reliability of the system is to identify the optimal number of DGs and their suitable locations in the system. To accomplish the aforementioned process and to evaluate the amount of power to be generated, a new method is proposed using Genetic Algorithm. The reliability parameters considered are EENS and ECOST. The proposed method is tested for IEEE 30 bus system, by connecting optimal number of DGs in the system. The results showed a considerable reduction in the total power loss in the system, stable voltage profiles and improved reliability indices. © 2012 Praise Worthy Prize S.r.l. - All rights reserved. Source


Chandrashekhar Reddy S.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Jaya Laxmi A.,JNTUH College of Engineering
European Journal of Scientific Research | Year: 2012

In order to reduce the power losses and to improve the voltage in the distribution system, distributed generators (DGs) are connected to load bus. To reduce the total power losses in the system, the most important process is to identify the proper location for fixing DGs and amount of power to be generated by those DGs. By considering the above objective, a hybrid technique was proposed which includes genetic algorithm and neural network. In the proposed method, genetic algorithm and neural network identifies the possible locations for fixing DGs and the amount of power generated to be by DG. By fixing DGs at suitable locations and also evaluating generating power based on the load conditions, the total power loss in the system can be reduced and the voltage in the buses can be improved. Thus, due to these two improvements, the power quality of the system improves. The proposed method is tested for different load conditions by connecting one DG, two DGs and three DGs in the system. © EuroJournals Publishing, Inc. 2012. Source


Reddy S.C.,Christu Jyoti Institute of Technology and Science | Prasad P.V.N.,Osmania University | Laxmi A.J.,University of Hyderabad
2012 IEEE 7th International Conference on Industrial and Information Systems, ICIIS 2012 | Year: 2012

Distributed Generators (DGs) are now commonly used in distribution systems to reduce the power disruption in the power system network. Due to the installation of DGs in the system, the total power loss can be reduced and voltage profile of the buses and reliability of the system can be improved. The significant process to decrease the total power loss and to improve the power quality of the system is to identify the optimal number of DGs and their suitable locations in the system. To accomplish the aforementioned process and to evaluate the amount of power to be generated, a new method is proposed using Particle Swarm Optimization. The proposed method is tested for IEEE 30 bus system, by connecting optimal number of DGs in the system. The results showed a considerable reduction in the total power loss in the system and improved voltage profiles of the buses and reliability indices. © 2012 IEEE. Source

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